In [1]:
import numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt
In [2]:
import matplotlib as plt
import geopandas 
data = pd.read_csv('death_cause_brazil.csv')
In [3]:
data.head()
Out[3]:
date state gender age color cause total
0 2020-01-01 AC F 60 - 69 East asian Septicemia 1
1 2019-01-01 AC F 80 - 89 White Hearth attack 1
2 2019-01-01 AC F 30 - 39 Indigenous Others 1
3 2019-01-01 AC F 70 - 79 Mixed Cardiogenic shock 1
4 2020-01-01 AC F 70 - 79 Mixed Pneumonia 1
In [4]:
sumdata = data['total'].sum()
print(sumdata)

2056363
In [5]:
data = data.dropna()
In [6]:
sumdata = data['total'].sum()
print(sumdata)

2056363
In [7]:
# Deaths 2019 brazil = 1349802
# Didn't have any case in 2019 of corona in brazil 
date2019 = pd.DataFrame({'date': pd.date_range(start='01-01-2019', end='12-31-2019')})
date2019['date'].groupby(date2019.date.dt.to_period("M")).agg('count')
Out[7]:
date
2019-01    31
2019-02    28
2019-03    31
2019-04    30
2019-05    31
2019-06    30
2019-07    31
2019-08    31
2019-09    30
2019-10    31
2019-11    30
2019-12    31
Freq: M, Name: date, dtype: int64
In [8]:
#data0 = data[['date', 'cause','total']].groupby(['cause','date']).mean()
#data0
In [9]:
# Brazil didn't have no cause of corona in 2019.drop 2019
data = data[~data.date.str.contains("2019")]
In [10]:
date_total = data[['date','total']].groupby(['date']).mean()
date_total
Out[10]:
total
date
2020-01-01 1.921719
2020-01-02 1.910909
2020-01-03 1.841687
2020-01-04 1.790528
2020-01-05 1.841709
... ...
2020-09-11 1.454783
2020-09-12 1.400939
2020-09-13 1.358079
2020-09-14 1.228145
2020-09-15 1.025316

259 rows × 1 columns

In [11]:
date_total.plot( figsize=(10,10));
2021-02-18T14:43:44.776887 image/svg+xml Matplotlib v3.3.3, https://matplotlib.org/
In [12]:
date_gender = data[['date','gender']].groupby(['gender']).count()
date_gender
Out[12]:
date
gender
F 230793
M 266019
In [13]:
date_gender.plot(kind='bar')
Out[13]:
<AxesSubplot:xlabel='gender'>
2021-02-18T14:43:45.282670 image/svg+xml Matplotlib v3.3.3, https://matplotlib.org/
In [14]:
data0 = data[['date', 'cause','total']].groupby(['cause','date']).count()
data0
Out[14]:
total
cause date
Cardiogenic shock 2020-01-01 75
2020-01-02 70
2020-01-03 77
2020-01-04 84
2020-01-05 63
... ... ...
Undetermined 2020-09-11 12
2020-09-12 7
2020-09-13 7
2020-09-14 4
2020-09-15 1

3397 rows × 1 columns

In [15]:
#data = data.drop(columns='date', axis=1)
In [16]:
sumdata = data['total'].sum()
print(sumdata)

911136
In [17]:
data.head()
Out[17]:
date state gender age color cause total
0 2020-01-01 AC F 60 - 69 East asian Septicemia 1
4 2020-01-01 AC F 70 - 79 Mixed Pneumonia 1
5 2020-01-01 AC F < 9 Mixed Pneumonia 1
6 2020-01-01 AC F < 9 Mixed Septicemia 1
7 2020-01-01 AC F 40 - 49 Mixed Stroke 1
In [18]:
data1 = data[['gender', 'color', 'cause', 'total']].groupby(['color','gender', 'cause']).agg({'total': ['sum']})

data1.sort_values(by=('total', 'sum'))
#race, gender and cause(Corona) 2019 to 2020
Out[18]:
total
sum
color gender cause
Indigenous M Covid (hearth attack) 2
F Covid (hearth attack) 2
M Undetermined 4
F Undetermined 5
Sudden death 7
... ... ... ...
Ignored M Others 34200
Mixed F Others 36599
M Others 56018
White F Others 65366
M Others 73934

168 rows × 1 columns

In [19]:
data2 = data[['gender', 'color', 'cause', 'total']].groupby(['cause','gender', 'color']).mean()

data2.sort_values('total', ascending=False)
# by Cause gender color . Corona Deaths in Brazil
Out[19]:
total
cause gender color
Others M White 3.340593
F White 3.176808
M Ignored 3.066990
Covid M White 2.823645
Others F Ignored 2.781824
... ... ... ...
Stroke M Indigenous 1.000000
Cardiopathy M Indigenous 1.000000
Covid (hearth attack) F Indigenous 1.000000
East asian 1.000000
Pneumonia M Indigenous 1.000000

168 rows × 1 columns

In [20]:
data3 = data[['cause', 'total']].groupby('cause').count()
data3.sort_values('total', ascending= False)
Out[20]:
total
cause
Others 129293
Pneumonia 56374
Septicemia 55990
Stroke 44309
Covid 42255
Hearth attack 39390
Respiratory failure 38290
Covid (stroke) 24087
Cardiopathy 22535
Cardiogenic shock 18535
Sars 9985
Sudden death 7967
Undetermined 5638
Covid (hearth attack) 2164
In [21]:
data3.plot(kind='bar')
Out[21]:
<AxesSubplot:xlabel='cause'>
2021-02-18T14:43:46.764122 image/svg+xml Matplotlib v3.3.3, https://matplotlib.org/
In [22]:
color_cause = data[['color', 'cause', 'total']].groupby(['color', 'cause']).count()
color_cause
Out[22]:
total
color cause
Black Cardiogenic shock 1529
Cardiopathy 2102
Covid 4643
Covid (hearth attack) 164
Covid (stroke) 2415
... ... ...
White Sars 3753
Septicemia 22087
Stroke 17078
Sudden death 3461
Undetermined 2044

84 rows × 1 columns

In [23]:
color_cause.plot(kind='bar', figsize=(20, 20))
Out[23]:
<AxesSubplot:xlabel='color,cause'>
2021-02-18T14:43:48.729391 image/svg+xml Matplotlib v3.3.3, https://matplotlib.org/
In [24]:
data4 = data[['age', 'cause', 'total']].groupby(['age', 'cause']).count()
data4
Out[24]:
total
age cause
10 - 19 Cardiogenic shock 113
Cardiopathy 33
Covid 224
Covid (stroke) 60
Hearth attack 70
... ... ...
N/I Sars 36
Septicemia 184
Stroke 297
Sudden death 36
Undetermined 136

167 rows × 1 columns

In [25]:
age = data[['age','total']].groupby('age').count()
age
#death in Brazil(corona virus) by age
Out[25]:
total
age
10 - 19 5303
20 - 29 12894
30 - 39 22154
40 - 49 38037
50 - 59 61091
60 - 69 84774
70 - 79 95873
80 - 89 91149
90 - 99 51063
< 9 20537
> 100 7253
N/I 6684
In [26]:
age.plot(kind='bar')
Out[26]:
<AxesSubplot:xlabel='age'>
2021-02-18T14:43:52.078426 image/svg+xml Matplotlib v3.3.3, https://matplotlib.org/
In [27]:
gender = data[['age','gender', 'total']].groupby(['age','gender']).count()
gender.plot(kind='bar')
#Deaths in Brazil in 2019 and 2020 by age and gender
Out[27]:
<AxesSubplot:xlabel='age,gender'>
2021-02-18T14:43:52.628838 image/svg+xml Matplotlib v3.3.3, https://matplotlib.org/
In [28]:
color = data[['color','total']].groupby(['color']).count()
color.plot(kind='bar')
Out[28]:
<AxesSubplot:xlabel='color'>
2021-02-18T14:43:53.180139 image/svg+xml Matplotlib v3.3.3, https://matplotlib.org/
In [29]:
state = data[['state','total']].groupby('state').count()
state
Out[29]:
total
state
AC 2952
AL 10925
AM 9955
AP 1778
BA 27577
CE 22522
DF 10896
ES 15970
GO 19887
MA 13215
MG 39797
MS 9834
MT 8874
PA 16280
PB 15749
PE 31896
PI 7455
PR 29228
RJ 55156
RN 10381
RO 5259
RR 1741
RS 26960
SC 16706
SE 7906
SP 74273
TO 3640
In [30]:
state.plot(kind='pie', y='total', figsize=(20, 10))
Out[30]:
<AxesSubplot:ylabel='total'>
2021-02-18T14:43:54.240393 image/svg+xml Matplotlib v3.3.3, https://matplotlib.org/
In [31]:
color_total = data[['color', 'total']].groupby(['color']).count()
color_total.plot(kind='pie', y = 'total', figsize=(10,8))
Out[31]:
<AxesSubplot:ylabel='total'>
2021-02-18T14:43:55.635338 image/svg+xml Matplotlib v3.3.3, https://matplotlib.org/
In [32]:
color_total = data[['gender', 'total']].groupby(['gender']).count()
color_total.plot(kind='bar', figsize=(10,8))
Out[32]:
<AxesSubplot:xlabel='gender'>
2021-02-18T14:43:56.165645 image/svg+xml Matplotlib v3.3.3, https://matplotlib.org/
In [33]:
ageGender = data[['gender','age' ,'total']].groupby(['gender','age']).count()
ageGender.plot(kind='bar', figsize=(10,8))
Out[33]:
<AxesSubplot:xlabel='gender,age'>
2021-02-18T14:43:56.638166 image/svg+xml Matplotlib v3.3.3, https://matplotlib.org/
In [34]:
ageGender = data[['gender','age' ,'total']].groupby(['gender','age']).mean()
ageGender.plot(kind='bar', figsize=(10,8))
Out[34]:
<AxesSubplot:xlabel='gender,age'>
2021-02-18T14:43:57.441553 image/svg+xml Matplotlib v3.3.3, https://matplotlib.org/
In [35]:
# drop cause: unknown, others, Undetermined, Sudden death because they are not clear causes
newdata = data[~data['cause'].isin(['Unknwon'])]
newdata = newdata[~newdata['cause'].isin(['Others'])]
newdata = newdata[~newdata['cause'].isin(['Undetermined'])]
newdata = newdata[~newdata['cause'].isin(['Sudden death'])]
In [36]:
sumdata2 = newdata['total'].sum()
print(sumdata2)

581149
In [37]:
dataCause = newdata[['cause', 'total']].groupby('cause').count()
dataCause.sort_values('total', ascending= False)
Out[37]:
total
cause
Pneumonia 56374
Septicemia 55990
Stroke 44309
Covid 42255
Hearth attack 39390
Respiratory failure 38290
Covid (stroke) 24087
Cardiopathy 22535
Cardiogenic shock 18535
Sars 9985
Covid (hearth attack) 2164
In [38]:
dataCause.plot(kind='bar', figsize=(10,10))
Out[38]:
<AxesSubplot:xlabel='cause'>
2021-02-18T14:43:58.424037 image/svg+xml Matplotlib v3.3.3, https://matplotlib.org/
In [39]:
color_gender = newdata[['gender', 'total']].groupby(['gender']).count()
color_gender.plot(kind='bar', figsize=(10,8))
Out[39]:
<AxesSubplot:xlabel='gender'>
2021-02-18T14:43:58.879735 image/svg+xml Matplotlib v3.3.3, https://matplotlib.org/
In [40]:
color_total2 = newdata[['color', 'total']].groupby(['color']).count()
color_total2.plot(kind='pie', y = 'total', figsize=(10,8))
Out[40]:
<AxesSubplot:ylabel='total'>
2021-02-18T14:43:59.353828 image/svg+xml Matplotlib v3.3.3, https://matplotlib.org/
In [41]:
color_age_total2 = newdata[['age', 'total']].groupby(['age']).count()
color_age_total2.plot(kind='bar', figsize=(10,8))
Out[41]:
<AxesSubplot:xlabel='age'>
2021-02-18T14:43:59.901390 image/svg+xml Matplotlib v3.3.3, https://matplotlib.org/
In [42]:
color_age_gender2 = newdata[['age', 'gender', 'total']].groupby(['age','gender']).count()
color_age_gender2.plot(kind='bar', figsize=(10,8))
Out[42]:
<AxesSubplot:xlabel='age,gender'>
2021-02-18T14:44:00.553858 image/svg+xml Matplotlib v3.3.3, https://matplotlib.org/
In [43]:
state2 = newdata[['state','total']].groupby('state').count()
state2
Out[43]:
total
state
AC 1766
AL 7390
AM 6673
AP 1325
BA 19438
CE 15409
DF 7586
ES 11345
GO 14596
MA 8687
MG 28285
MS 6976
MT 4271
PA 11703
PB 10625
PE 23302
PI 5268
PR 20239
RJ 41326
RN 7143
RO 3468
RR 1189
RS 19127
SC 11764
SE 5205
SP 57375
TO 2433
In [44]:
state2.plot(kind='bar', figsize=(10,8))
Out[44]:
<AxesSubplot:xlabel='state'>
2021-02-18T14:44:01.400533 image/svg+xml Matplotlib v3.3.3, https://matplotlib.org/
In [45]:
#rate of deaths = https://www.cdc.gov/csels/dsepd/ss1978/lesson3/section3.html
# Brazil population 2020 = 211.755.692 https://agenciadenoticias.ibge.gov.br/agencia-sala-de-imprensa/2013-agencia-de-noticias/releases/28668-ibge-divulga-estimativa-da-populacao-dos-municipios-para-2020
#1.314.097... - Veja mais em https://noticias.uol.com.br/saude/ultimas-noticias/redacao/2020/12/13/com-13-mi-registros-ate-novembro2020-ja-bate-recorde-de-mortes-no-brasil.htm?cmpid=copiaecola
deaths_total_2020 = 1314097
sumdeathCorona2 = newdata['total'].sum()
population =211755692

#rate population 
rate = (sumdeathCorona2*100000)/(population*100000)
print(rate)
crude_rate = (deaths_total_2020*100000)/(population*100000)
print(crude_rate)

0.002744431540475427
0.006205722205568859
In [46]:
age_rate = newdata[['age','total']].groupby('age').count()

age_rate
Out[46]:
total
age
10 - 19 2351
20 - 29 6146
30 - 39 12998
40 - 49 25070
50 - 59 43879
60 - 69 64451
70 - 79 74280
80 - 89 70403
90 - 99 37517
< 9 11172
> 100 4360
N/I 1287
In [47]:
save = newdata['age'].value_counts()
save
Out[47]:
70 - 79    74280
80 - 89    70403
60 - 69    64451
50 - 59    43879
90 - 99    37517
40 - 49    25070
30 - 39    12998
< 9        11172
20 - 29     6146
> 100       4360
10 - 19     2351
N/I         1287
Name: age, dtype: int64
In [48]:
date_total2 = newdata[['date','total']].groupby(['date']).mean()
date_total2
Out[48]:
total
date
2020-01-01 1.702726
2020-01-02 1.678967
2020-01-03 1.634369
2020-01-04 1.603433
2020-01-05 1.647834
... ...
2020-09-11 1.359155
2020-09-12 1.277487
2020-09-13 1.289231
2020-09-14 1.179412
2020-09-15 1.000000

259 rows × 1 columns

In [49]:
date_total2.plot()
Out[49]:
<AxesSubplot:xlabel='date'>
2021-02-18T14:44:02.376194 image/svg+xml Matplotlib v3.3.3, https://matplotlib.org/
In [50]:
import geopandas as gpd

fp = "LM_UF.shp"
map_df = gpd.read_file(fp, encoding='utf-8')
map_df.head()
Out[50]:
PK nome codigoUf siglaUf PRIMARYIND geometry
0 1 b'Para\xedba' 25 PB 1 MULTIPOLYGON (((-34.92545 -6.68227, -34.92778 ...
1 2 b'Paran\xe1' 41 PR 2 MULTIPOLYGON (((-48.39160 -25.32784, -48.39677...
2 3 Sergipe 28 SE 3 MULTIPOLYGON (((-37.02340 -10.93902, -37.02570...
3 4 Rio de Janeiro 33 RJ 4 MULTIPOLYGON (((-40.97131 -22.05305, -40.97191...
4 5 Rio Grande do Sul 43 RS 5 MULTIPOLYGON (((-51.28678 -30.06487, -51.28926...
In [51]:
map_df.plot()
Out[51]:
<AxesSubplot:>
2021-02-18T14:44:04.077504 image/svg+xml Matplotlib v3.3.3, https://matplotlib.org/
In [52]:
import numpy as np
import pandas as pd
import shapefile as shp
import matplotlib.pyplot as plt
import seaborn as sns
In [53]:
map_df.plot()
Out[53]:
<AxesSubplot:>
2021-02-18T14:44:09.110216 image/svg+xml Matplotlib v3.3.3, https://matplotlib.org/
In [54]:
map_df.head(10)
Out[54]:
PK nome codigoUf siglaUf PRIMARYIND geometry
0 1 b'Para\xedba' 25 PB 1 MULTIPOLYGON (((-34.92545 -6.68227, -34.92778 ...
1 2 b'Paran\xe1' 41 PR 2 MULTIPOLYGON (((-48.39160 -25.32784, -48.39677...
2 3 Sergipe 28 SE 3 MULTIPOLYGON (((-37.02340 -10.93902, -37.02570...
3 4 Rio de Janeiro 33 RJ 4 MULTIPOLYGON (((-40.97131 -22.05305, -40.97191...
4 5 Rio Grande do Sul 43 RS 5 MULTIPOLYGON (((-51.28678 -30.06487, -51.28926...
5 6 Santa Catarina 42 SC 6 MULTIPOLYGON (((-48.59134 -25.97978, -48.59299...
6 7 b'S\xe3o Paulo' 35 SP 7 MULTIPOLYGON (((-44.90589 -23.35318, -44.90678...
7 8 b'Amap\xe1' 16 AP 8 MULTIPOLYGON (((-50.50034 2.09786, -50.50185 2...
8 9 b'Cear\xe1' 23 CE 9 MULTIPOLYGON (((-40.20093 -2.81293, -40.20502 ...
9 10 Distrito Federal 53 DF 10 MULTIPOLYGON (((-29.34586 0.91777, -29.34587 0...
In [55]:
newdata.head()
Out[55]:
date state gender age color cause total
0 2020-01-01 AC F 60 - 69 East asian Septicemia 1
4 2020-01-01 AC F 70 - 79 Mixed Pneumonia 1
5 2020-01-01 AC F < 9 Mixed Pneumonia 1
6 2020-01-01 AC F < 9 Mixed Septicemia 1
7 2020-01-01 AC F 40 - 49 Mixed Stroke 1
In [56]:
state2 = newdata[['state','total']].groupby(('state'),as_index=False).count()
state2
Out[56]:
state total
0 AC 1766
1 AL 7390
2 AM 6673
3 AP 1325
4 BA 19438
5 CE 15409
6 DF 7586
7 ES 11345
8 GO 14596
9 MA 8687
10 MG 28285
11 MS 6976
12 MT 4271
13 PA 11703
14 PB 10625
15 PE 23302
16 PI 5268
17 PR 20239
18 RJ 41326
19 RN 7143
20 RO 3468
21 RR 1189
22 RS 19127
23 SC 11764
24 SE 5205
25 SP 57375
26 TO 2433
In [57]:
#join the data 
merged = map_df.set_index('siglaUf').join(state2.set_index("state"))
merged.head()
Out[57]:
PK nome codigoUf PRIMARYIND geometry total
siglaUf
PB 1 b'Para\xedba' 25 1 MULTIPOLYGON (((-34.92545 -6.68227, -34.92778 ... 10625
PR 2 b'Paran\xe1' 41 2 MULTIPOLYGON (((-48.39160 -25.32784, -48.39677... 20239
SE 3 Sergipe 28 3 MULTIPOLYGON (((-37.02340 -10.93902, -37.02570... 5205
RJ 4 Rio de Janeiro 33 4 MULTIPOLYGON (((-40.97131 -22.05305, -40.97191... 41326
RS 5 Rio Grande do Sul 43 5 MULTIPOLYGON (((-51.28678 -30.06487, -51.28926... 19127
In [58]:
#create map
death_brazil = 'total'
vmin, vmax = 1000,25000
fig, ax = plt.subplots(1, figsize=(10,6))
merged.plot(column=death_brazil, cmap='Reds', linewidth=0.8, ax=ax, edgecolor='0.8')
ax.axis('off')
ax.set_title('Mortes no Brasil', fontdict={'fontsize': '25', 'fontweight' : '3'})

#color legend
sm = plt.cm.ScalarMappable(cmap='Reds', norm=plt.Normalize(vmin=vmin, vmax=vmax))
# empty array for the data range
sm._A = []
# add the colorbar to the figure
cbar = fig.colorbar(sm)
2021-02-18T14:44:13.503910 image/svg+xml Matplotlib v3.3.3, https://matplotlib.org/
In [59]:
ax.axis('off')
Out[59]:
(-76.25697265565714, -26.582568221505483, -35.7018588272153, 7.22888375003369)
In [60]:
fig.savefig('map_export.png', dpi=300)
In [ ]: